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Ranking discrete outcome alternatives with partially quantified uncertainty

Citation

Tenekedjiev, KI and Nikolova, ND, Ranking discrete outcome alternatives with partially quantified uncertainty, International Journal of General Systems, 37, (2) pp. 249-274. ISSN 0308-1079 (2008) [Refereed Article]

Copyright Statement

Copyright 2008 Taylor & Francis

DOI: doi:10.1080/03081070701409046

Abstract

Real decision makers (DM) partially quantify uncertainty as probability uncertainty intervals. Then alternatives are modeled as fuzzy-rational lotteries. Those can be approximated by standard lotteries with point-estimate probabilities, referred as classical-risky. Ranking the approximating classical-risky lotteries is a problem under risk, solved by expected utility. However, the approximation itself is a problem under strict uncertainty. Here this part of the problem is solved by criteria under strict uncertainty, which if not perfectly rational, are well worked descriptive methods with known properties. The proposed Laplace, Wald, maximax and Hurwiczα expected utility criteria for prescriptive ranking of fuzzy-rational lotteries allow the DM to control the approximation of alternatives with partially quantified uncertainty in consistency with her/his degree of belief and with his optimism–pessimism attitude. That makes them superior to the widespread abandoned-m and normalized mean criteria, which often violate the intrinsic subjective probabilities of the DM. The proposed criteria are generalizations of expected utility criterion under risk and of their standard versions under strict uncertainty.

Item Details

Item Type:Refereed Article
Keywords:fuzzy rational alternatives, expected utility
Research Division:Information and Computing Sciences
Research Group:Information Systems
Research Field:Decision Support and Group Support Systems
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Information and Computing Sciences
UTAS Author:Tenekedjiev, KI (Professor Kiril Tenekedjiev)
UTAS Author:Nikolova, ND (Professor Nataliya Nikolova)
ID Code:127968
Year Published:2008
Web of Science® Times Cited:9
Deposited By:AMC Governance Office
Deposited On:2018-08-26
Last Modified:2018-09-06
Downloads:0

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